Keiser University’s Bachelor of Science degree in Artificial Intelligence (AI), with concentrations in Machine Learning and Data Science, is designed to address the increasing demand for AI specialists. Students develop the appropriate skills by using hands-on activities and analytical exercises while working with natural language processing, voice recognition, simulated thinking patterns, decision behaviors, machine learning, and deep learning utilizing big data sets. The Bachelor of Science in Artificial Intelligence program targets students interested in the study, development, and deployment of AI applications in a variety of industries. Students learn the necessary skills and practical abilities to understand, identify, select, and apply the appropriate tools, algorithms, methodologies, and ethical standards to projects related to this emerging field.
The curriculum enables students to solve and navigate complex scenarios that require drawing inferences appropriate to a given situation, performing decision-making using complex and changing data sets, discovering meaning, and generalizing conclusions based upon learned events and experience. The degree program covers general and applied AI fundamentals, including core programming languages such as Python, and platforms used in computer science specific to the sub-field of Artificial Intelligence.
The following objectives are designed to meet Keiser University’s mission and its goals:
To receive a Bachelor of Science degree in Artificial Intelligence, students must earn 120 semester credit hours. Program requirements are as follows:
Artificial Intelligence Major (33.0 credit hours) 11 courses | |
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Python Programming | 4.0 credit hours |
Essentials of Networking | 3.0 credit hours |
Data Structures & Algorithms | 3.0 credit hours |
Java Programming I | 4.0 credit hours |
Discrete Mathematics and Probability | 4.0 credit hours |
Multi-User Operating Systems | 3.0 credit hours |
Principles of Information Security | 3.0 credit hours |
Concepts of AI Programming | 4.0 credit hours |
Introduction to Machine Learning | 3.0 credit hours |
Artificial Intelligence I | 3.0 credit hours |
Web Development I | 4.0 credit hours |
Behavioral/Social Science (3.0 credit hours) 1 course | |
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Introduction to Psychology | 3.0 credit hours |
Political Science | 3.0 credit hours |
Sociology | 3.0 credit hours |
Communications (3.0 credit hours) 1 course | |
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Speech | 3.0 credit hours |
Computers (3.0 credit hours) 1 course | |
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Introduction to Computers | 3.0 credit hours |
English (6.0 credit hours) 2 courses | |
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English Composition I | 3.0 credit hours |
English Composition II | 3.0 credit hours |
Humanities/Fine Arts (3.0 credit hours) 1 course | |
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American Literature | 3.0 credit hours |
Contemporary World Literature | 3.0 credit hours |
Mathematics (6.0 credit hours) 2 courses | |
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College Algebra | 3.0 credit hours |
Statistics | 3.0 credit hours |
Natural Science (6.0 credit hours) 2 courses | |
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General Biology | 3.0 credit hours |
Advanced Biology | 3.0 credit hours |
General Chemistry | 3.0 credit hours |
Advanced Chemistry | 3.0 credit hours |
Upper Division General Education (6.0 credit hours) 2 courses | |
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Intermediate Statistics | 3.0 credit hours |
Critical Thinking | 3.0 credit hours |
Artificial Intelligence Major (33.0 credit hours) 11 courses | |
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Theory of Computation | 3.0 credit hours |
Deep Learning | 3.0 credit hours |
Artificial Intelligence II | 3.0 credit hours |
Legal and Social Issues in Computing | 3.0 credit hours |
Non-Relational Data Stores | 3.0 credit hours |
Cloud and Internet Computing | 3.0 credit hours |
Data Mining and Warehousing | 3.0 credit hours |
Neural Networks | 3.0 credit hours |
Compiler Construction | 3.0 credit hours |
Natural Language Processing | 3.0 credit hours |
Artificial Intelligence Capstone | 3.0 credit hours |
Upper Division Concentration (12.0 credit hours) 4 courses | |
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Machine Learning | |
Machine Learning Frameworks | 3.0 credit hours |
Machine Learning | 3.0 credit hours |
Advanced Machine Learning | 3.0 credit hours |
Data Mining and Machine Learning | 3.0 credit hours |
Data Science | |
Data Visualization | 3.0 credit hours |
Methods and Tools for Data Science | 3.0 credit hours |
Data Science and Analytics | 3.0 credit hours |
Big Data Analytics | 3.0 credit hours |
Credit hours in parentheses indicate the required number of credit hours in each discipline.
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